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Articles Published Processes
1/26/2026 6:06:22 AM | Browse: 2 | Download: 0
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Received |
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2025-10-20 02:08 |
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Peer-Review Started |
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2025-10-20 02:08 |
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First Decision by Editorial Office Director |
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2025-11-11 08:49 |
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Return for Revision |
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2025-11-11 08:49 |
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Revised |
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2025-11-17 17:51 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-01-06 02:48 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-01-06 08:10 |
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Articles in Press |
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2026-01-06 08:10 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-01-14 10:37 |
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Publish the Manuscript Online |
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2026-01-26 06:06 |
| ISSN |
1949-8470 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Computer Science, Artificial Intelligence |
| Manuscript Type |
Retrospective Cohort Study |
| Article Title |
Deep learning-based imaging model to predict early hematoma enlargement and hospital mortality in spontaneous intracerebral hemorrhage
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Yu-Han Yang and Yuan Li |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Yu-Han Yang, MD, West China Hospital, Sichuan University, No. 17 People’s South Road, Chengdu 6100041, Sichuan Province, China. yyh_1023@163.com |
| Key Words |
Intracerebral hemorrhage; Enlargement; Computed tomography; Radiomics; Deep learning; Hospital mortality |
| Core Tip |
In this work, we developed quantitative and easy-to-reach prediction tools for early hematoma enlargement in spontaneous intracerebral hemorrhage based on the radiological features from deep learning or handcrafted radiomics methods, and validated the predictive models in an independent cohort to assure their discriminative capacities. The artificial intelligence based computer aided diagnosis methods we used to predict hematoma enlargement in spontaneous intracerebral hemorrhage on computed tomography images would assist making decisions about whether clinicians should implement positive surgical intervention or not at early stage once admission. |
| Publish Date |
2026-01-26 06:06 |
| Citation |
Yang YH, Li Y. Deep learning-based imaging model to predict early hematoma enlargement and hospital mortality in spontaneous intracerebral hemorrhage. World J Radiol 2026; 18(1): 115504 |
| URL |
https://www.wjgnet.com/1949-8470/full/v18/i1/115504.htm |
| DOI |
https://dx.doi.org/10.4329/wjr.v18.i1.115504 |
© 2004-2026 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
California Corporate Number: 3537345
